A Complete Guide to BERT with Code | by Bradney Smith

History, Architecture, Pre-training, and Fine-tuning – Part 4 of the “LLMs from Scratch” series is a comprehensive guide to understanding and constructing Large Language Models. If you are interested in delving deeper into how these models operate, it is recommended that you read about Bidirectional Encoder Representations from Transformers (BERT), a groundbreaking LLM developed by Google AI Language that has significantly advanced Natural Language Processing (NLP). Various models in recent years have drawn inspiration from or have directly improved upon BERT, such as RoBERTa, ALBERT, and DistilBERT, among others. BERT was introduced shortly after OpenAI’s Generative Pre-trained Transformer (GPT), with both models building upon the Transformer architecture proposed the year prior. While GPT focused on Natural Language Generation (NLG), BERT prioritized Natural Language Understanding (NLU), reshaping the NLP landscape and marking significant milestones in machine learning progression.

This article will delve into the history of BERT, providing insights into the landscape at the time of its inception. This will offer a complete view not only of the architectural decisions made by the authors of the paper but also an understanding of how to train and fine-tune BERT for various industry and hobbyist applications. A detailed examination of the architecture with diagrams will be provided, along with code examples to showcase fine-tuning BERT for tasks like sentiment analysis.

The BERT model can be characterized by four main features: an Encoder-only architecture, a unique Pre-training approach, the ability for Model fine-tuning, and the utilization of bidirectional context. These features were deliberate design choices made by the authors of the paper, influenced by the context in which the model was created. Each of these features will be explored in detail, shedding light on how they were either inspired by BERT’s contemporaries or aimed at enhancing their capabilities.

1.1 – Encoder-Only Architecture:
The introduction of the Transformer in 2017 sparked a race to develop new models building on its innovative design. OpenAI’s GPT, a decoder-only model excelling in NLG, was launched in June 2018, followed by Google’s release of BERT, an encoder-only model focused on NLU. Decoder-only models, like GPT, are adept at generating output sequences based on input prompts, making them suitable for tasks such as conversational chatbots or machine translation. On the other hand, encoder-only models, like BERT, excel at making predictions within input sequences, making them ideal for NLU tasks like Named Entity Recognition and sentiment analysis. The architecture choice of an encoder-only model aimed to enhance BERT’s performance compared to GPT, specifically for token-level tasks.

1.2 – Pre-training Approach:
GPT popularized model pre-training, a technique that involves training a large model to acquire a broad understanding of language, leading to a task-agnostic foundational model. Copies of this foundational model can be fine-tuned for specific tasks by training only the linear layer, while keeping the rest of the model frozen. This pre-training approach significantly reduces training time and costs, compared to training new architectures from scratch for each task. The effectiveness of pre-training was firmly established by OpenAI’s seminal paper, leading to its widespread adoption in subsequent language modeling endeavors, including BERT.

1.3 – Model Fine-tuning:
Fine-tuning has become a standard practice in model development, significantly reducing training time and compute costs. Prior to the rise of pre-training, new model architectures were introduced for each NLP task, making development resource-intensive. Fine-tuning allows for efficient adaptation of pre-trained models to specific tasks, improving performance without the need to train new models from scratch. This paradigm shift has revolutionized the approach to language modeling and has become a fundamental aspect of model development strategies.

In conclusion, BERT’s history, architecture, pre-training approach, and model fine-tuning have played pivotal roles in advancing NLP and machine learning. By understanding these key features, developers and researchers can leverage BERT’s capabilities for a wide range of applications, from industry tasks to hobbyist projects. Further exploration of BERT and related models is recommended for those interested in the cutting-edge developments in the field of Large Language Models.

Source link